library(tidyverse)
library(survival)
library(survminer)
library(kableExtra)
d<-data.frame(
time=c(2,3,2,9,16,18,7,16,5,5),
event=c(1,0,1,1,1,0,1,1,1,0)
)
so<-Surv(d$time,d$event)
so |> t() |> as.data.frame() |> kable() |> kable_classic()
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V1
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fit<-survfit(so~1,data = d,type="kaplan-meier")
fit_frame<-data.frame(
time=fit$time,
risk=fit$n.risk,
event=fit$n.event,
survival=fit$surv,
ci_lower=fit$lower,
ci_uper=fit$upper
) |> filter(event!=0) |> kable() |> kable_classic()
fit_frame
|
time
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risk
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event
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survival
|
ci_lower
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ci_uper
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2
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10
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2
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0.8000000
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0.5868177
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1.0000000
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5
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7
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1
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0.6857143
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0.4447217
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1.0000000
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7
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5
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1
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0.5485714
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0.2962557
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1.0000000
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9
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4
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1
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0.4114286
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0.1782448
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0.9496683
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16
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3
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2
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0.1371429
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0.0225400
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0.8344350
|
ggsurvplot(fit = fit,censor=T,data = d,conf.int = F,ggtheme = theme_bw())
d2<-data.frame(
time=c(6,6,6,6, 7,9, 10,10, 11, 13,16,17, 19, 20, 22,23,25, 32, 32, 34, 35),
event=c(1,1,1,0, 1,0, 1,0, 0, 1,1,0, 0, 0, 1,1,0, 0, 0, 0, 0)
)
so_d2<-Surv(time=d2$time,event = d2$event)
so_d2 |> t() |> as.data.frame() |> kable() |> kable_classic()
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V1
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time
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fit_d2<-survfit(so_d2~1,data = d2,type="kaplan-meier")
frame_2<-data.frame(
time=fit_d2$time,
risk=fit_d2$n.risk,
event=fit_d2$n.event,
survival=fit_d2$surv,
ci_lower=fit_d2$lower,
ci_uper=fit_d2$upper,
nn=fit_d2$n.event/(fit_d2$n.event*(fit_d2$n.risk-fit_d2$n.event))
) |> mutate(
csum=cumsum(nn),
var=survival^2*csum
) |> filter(event!=0) |> kable(
col.names=c(
"Time",
"Risk",
"Event",
"$S(t)$",
"CI Lower",
"CI Upper",
"NN",
"Csum",
"var"
)
) |> kable_classic()
frame_2
|
Time
|
Risk
|
Event
|
\(S(t)\)
|
CI Lower
|
CI Upper
|
NN
|
Csum
|
var
|
|
6
|
21
|
3
|
0.8571429
|
0.7198171
|
1.0000000
|
0.0555556
|
0.0555556
|
0.0408163
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|
7
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17
|
1
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0.8067227
|
0.6531242
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0.9964437
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0.0625000
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0.1180556
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0.0768307
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10
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15
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1
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0.7529412
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0.5859190
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0.9675748
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0.0714286
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NaN
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NaN
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13
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12
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1
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0.6901961
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0.5096131
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0.9347692
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0.0909091
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NaN
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NaN
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16
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11
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1
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0.6274510
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0.4393939
|
0.8959949
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0.1000000
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NaN
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NaN
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22
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7
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1
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0.5378151
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0.3370366
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0.8582008
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0.1666667
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NaN
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NaN
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23
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6
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1
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0.4481793
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0.2487882
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0.8073720
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0.2000000
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NaN
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NaN
|
ggsurvplot(fit_d2,data = d2,censor=T,conf.int = F,ggtheme = theme_bw())
d3<-data.frame(
time=c(2,3,2,9,18,7,16,5,5,16),
event=c(1,0,1,1,0,1,1,1,0,1)
)
so_d3<-Surv(d3$time,d3$event)
so_d3 |> t() |> kable() |> kable_classic()
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time
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2
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2
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7
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status
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1
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0
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1
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1
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1
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1
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1
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1
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fit_d3<-survfit(so_d3~1,data=d3,type="kaplan-meier")
summary(fit_d3)
## Call: survfit(formula = so_d3 ~ 1, data = d3, type = "kaplan-meier")
##
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 2 10 2 0.800 0.126 0.5868 1.000
## 5 7 1 0.686 0.151 0.4447 1.000
## 7 5 1 0.549 0.172 0.2963 1.000
## 9 4 1 0.411 0.176 0.1782 0.950
## 16 3 2 0.137 0.126 0.0225 0.834
fit_frame3<-data.frame(
time=fit_d3$time,
risk=fit_d3$n.risk,
event=fit_d3$n.event,
survival=fit_d3$surv,
ci_lower=fit_d3$lower,
ci_uper=fit_d3$upper
)
fit_frame3 |> filter(event!=0) |> kable() |> kable_classic()
|
time
|
risk
|
event
|
survival
|
ci_lower
|
ci_uper
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2
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10
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2
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0.8000000
|
0.5868177
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1.0000000
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5
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7
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1
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0.6857143
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0.4447217
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1.0000000
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7
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5
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1
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0.5485714
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0.2962557
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1.0000000
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9
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4
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1
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0.4114286
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0.1782448
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0.9496683
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16
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3
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2
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0.1371429
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0.0225400
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0.8344350
|
ggsurvplot(fit_d3,data=d3,conf.int = F,censor=T,ggtheme = theme_bw(),surv.median.line = "hv")
d4<-data.frame(
time=c(2,3,2,9,18,7,16,5,5,16),
event=c(1,0,1,1,0,1,1,1,0,1)
)
so_d4<-Surv(d4$time,d4$event)
so_d4 |> t() |> kable() |> kable_classic()
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time
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2
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3
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status
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1
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0
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1
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1
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1
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1
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1
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0
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1
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fit_d4<-survfit(so_d4~1,data=so_d4,type="kaplan-meier")
fit_frame_4<-data.frame(
time=fit_d4$time,
risk=fit_d4$n.risk,
event=fit_d4$n.event,
survival=fit_d4$surv,
ci_lower=fit_d4$lower,
ci_uper=fit_d4$upper
)
fit_frame_4 |> filter(event!=0) |> kable() |> kable_classic()
|
time
|
risk
|
event
|
survival
|
ci_lower
|
ci_uper
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2
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10
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2
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0.8000000
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0.5868177
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1.0000000
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5
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7
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1
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0.6857143
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0.4447217
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1.0000000
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5
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1
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0.5485714
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0.2962557
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1.0000000
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9
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4
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1
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0.4114286
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0.1782448
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0.9496683
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16
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3
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2
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0.1371429
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0.0225400
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0.8344350
|
ggsurvplot(fit_d4,data=d4,conf.int = F,censor=T,ggtheme = theme_bw(),surv.median.line = "hv")
d5<-data.frame(
time=c(5,11,14,21,25,32,48,2,12,23,35),
event=c(1,1,1,1,1,1,1,0,0,0,0)
)
d5 |> t() |> kable() |> kable_classic()
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time
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5
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11
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14
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21
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25
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32
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48
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2
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12
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23
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35
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event
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1
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1
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1
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1
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1
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1
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1
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0
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0
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0
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0
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so_d5<-Surv(d5$time,d5$event)
so_d5 |>as.data.frame() |> kable() |> kable_classic()
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x
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5
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11
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14
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21
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25
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32
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48
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2+
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12+
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23+
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35+
|
fit_d5<-survfit(so_d5~1,data=d5,type="kaplan-meier")
fit_frame_5<-data.frame(
time=fit_d5$time,
risk=fit_d5$n.risk,
event=fit_d5$n.event,
survival=fit_d5$surv,
ci_lower=fit_d5$lower,
ci_uper=fit_d5$upper
)
fit_frame_5 |> filter(event!=0) |> kable() |> kable_classic()
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time
|
risk
|
event
|
survival
|
ci_lower
|
ci_uper
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5
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10
|
1
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0.9000000
|
0.7320116
|
1.0000000
|
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11
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9
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1
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0.8000000
|
0.5868177
|
1.0000000
|
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14
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7
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1
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0.6857143
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0.4447217
|
1.0000000
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21
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6
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1
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0.5714286
|
0.3258387
|
1.0000000
|
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25
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4
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1
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0.4285714
|
0.1930923
|
0.9512211
|
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32
|
3
|
1
|
0.2857143
|
0.0923355
|
0.8840871
|
|
48
|
1
|
1
|
0.0000000
|
NA
|
NA
|
ggsurvplot(fit_d5,data=d5,conf.int = F,censor=T,ggtheme = theme_bw(),surv.median.line = "hv")
d6<-data.frame(
time=c(10,13,18,19,23,30,36,38,54,56,59,75,73,97,104,107,107,107),
event=c(1,0,0,1,0,1,1,0,0,0,1,1,1,1,0,1,0,0)
)
so_d6<-Surv(d6$time,d6$event)
so_d6 |>as.data.frame() |> kable() |> kable_classic()
|
x
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10
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13+
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18+
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19
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23+
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30
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36
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38+
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54+
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56+
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59
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75
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73
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97
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104+
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107
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107+
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107+
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fit_d6<-survfit(so_d6~1,data=d6,type="kaplan-meier")
fit_frame_6<-data.frame(
time=fit_d6$time,
risk=fit_d6$n.risk,
event=fit_d6$n.event,
survival=fit_d6$surv,
ci_lower=fit_d6$lower,
ci_uper=fit_d6$upper
)
fit_frame_6 |> filter(event!=0) |> kable() |> kable_classic()
|
time
|
risk
|
event
|
survival
|
ci_lower
|
ci_uper
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|
10
|
18
|
1
|
0.9444444
|
0.8443382
|
1.0000000
|
|
19
|
15
|
1
|
0.8814815
|
0.7394986
|
1.0000000
|
|
30
|
13
|
1
|
0.8136752
|
0.6429314
|
1.0000000
|
|
36
|
12
|
1
|
0.7458689
|
0.5576521
|
0.9976121
|
|
59
|
8
|
1
|
0.6526353
|
0.4412662
|
0.9652515
|
|
73
|
7
|
1
|
0.5594017
|
0.3411298
|
0.9173349
|
|
75
|
6
|
1
|
0.4661681
|
0.2531720
|
0.8583600
|
|
97
|
5
|
1
|
0.3729345
|
0.1758973
|
0.7906891
|
|
107
|
3
|
1
|
0.2486230
|
0.0829480
|
0.7452069
|
ggsurvplot(fit_d6,data=d6,conf.int = F,censor=T,ggtheme = theme_bw(),surv.median.line = "hv")
ggsurvplot(fit_d6,data=d6,conf.int = F,censor=F,ggtheme = theme_bw(),fun = "cumhaz")
d7<-data.frame(
time=c(23,47,69,70,100,148,190,208,212,214),
event=c(1,1,1,0,0,1,0,0,0,0)
)
so_d7<-Surv(d7$time,d7$event)
so_d7 |>as.data.frame() |> kable() |> kable_classic()
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x
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23
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47
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69
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70+
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100+
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148
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190+
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208+
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212+
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214+
|
fit_d7<-survfit(so_d7~1,data=d7,type="kaplan-meier")
fit_frame_7<-data.frame(
time=fit_d7$time,
risk=fit_d7$n.risk,
event=fit_d7$n.event,
survival=fit_d7$surv,
ci_lower=fit_d7$lower,
ci_uper=fit_d7$upper
)
fit_frame_7 |> filter(event!=0) |> kable() |> kable_classic()
|
time
|
risk
|
event
|
survival
|
ci_lower
|
ci_uper
|
|
23
|
10
|
1
|
0.90
|
0.7320116
|
1
|
|
47
|
9
|
1
|
0.80
|
0.5868177
|
1
|
|
69
|
8
|
1
|
0.70
|
0.4665332
|
1
|
|
148
|
5
|
1
|
0.56
|
0.3081810
|
1
|
ggsurvplot(fit_d7,data=d7,conf.int = F,censor=T,ggtheme = theme_bw(),surv.median.line = "hv")
## Warning in .add_surv_median(p, fit, type = surv.median.line, fun = fun, :
## Median survival not reached.
ggsurvplot(fit_d7,data=d7,conf.int = F,censor=F,ggtheme = theme_bw(),fun = "cumhaz")
